Create a copy-paste prompt library for my HR team. Our organization and workforce are [ORGANIZATION CONTEXT]. Our policies and jurisdiction constraints are [POLICIES AND JURISDICTIONS]. The HR work we need help with is [PRIORITY WORK]. Our approved tone, data rules, and escalation path are [GUARDRAILS]. Our available source material is [SOURCE MATERIALS]. Produce 10 reusable prompts, each 120-180 words, for: job-description drafting; interview-plan design; structured interview feedback synthesis; candidate communication; onboarding plan; manager coaching conversation; performance-review draft; employee-policy FAQ; engagement-survey analysis; and workforce-planning scenario summary. Every prompt must identify inputs, output format, who must review it, and what it must not decide. Build in HR-specific safeguards: use job-related criteria; separate evidence from interpretation; avoid protected-class inferences; preserve reasonable-accommodation and leave escalation; do not rank candidates or recommend discipline from incomplete notes; and do not place confidential employee data into an unapproved tool.
For candidate and performance work, require a criteria-to-evidence table and flag gaps rather than filling them. For policies, quote or cite only the supplied policy text and mark jurisdiction-specific issues for counsel or HR leadership. End with a one-page routing guide showing which prompts need legal, employee-relations, compensation, security, or manager approval. Self-check before answering: confirm each prompt has a distinct HR use case and never asks the AI to make an employment decision. Ask up to three clarifying questions only if a required input is missing.
Fill in
| Placeholder | What to enter | Example |
|---|---|---|
| [ORGANIZATION CONTEXT] | Describe the company size, workforce type, countries or states, and HR operating model. | 320-person software company with US employees in 18 states and a 40-person UK team |
| [POLICIES AND JURISDICTIONS] | List relevant policies, collective agreements, regulated environments, and employment jurisdictions. | US handbook, UK employee handbook, California leave rules, and SOC 2 data-handling requirements |
| [PRIORITY WORK] | Name the HR tasks your team performs most often or needs to improve. | Hiring manager enablement, performance reviews, onboarding, and employee communications |
| [GUARDRAILS] | State privacy rules, approved tools, tone, retention rules, and escalation contacts. | Use approved enterprise AI only; no medical or accommodation details; employee relations reviews sensitive drafts |
| [SOURCE MATERIALS] | Paste approved job frameworks, policy excerpts, competency models, and communication examples. | Engineering competency matrix, approved job-description template, 2026 handbook excerpts, and onboarding checklist |
How to use
- Paste only approved, minimized information and replace employee names with identifiers where possible.
- Give the library your real competency model and policy excerpts so prompts do not rely on generic HR language.
- Check outputs against job-related criteria, policy text, and the required review owner before use.
- Follow up with: “Create a version of the interview feedback prompt for [ROLE] using these competencies: [COMPETENCIES].”
Variations
Interview kit
Use this when a hiring manager needs a structured, defensible interview process.
Create a structured interview kit for [ROLE] using [JOB REQUIREMENTS] and [COMPETENCY MODEL]. Produce a scorecard with 4-6 job-related competencies, behavioral questions, permitted follow-ups, positive and negative evidence examples, and a 1-5 anchored scoring guide. Include a post-interview evidence table that separates notes from ratings. Exclude questions about protected characteristics, family status, health, citizenship beyond authorized work eligibility, or other non-job-related matters. Flag any requirement that needs legal or HR review under [JURISDICTION].
Policy FAQ
Use this to turn approved policy text into an employee-facing explanation.
Turn the following approved policy excerpt into an employee FAQ: [POLICY TEXT]. The audience is [AUDIENCE], jurisdiction is [JURISDICTION], and tone is [TONE]. Produce 8-12 questions and answers using only the supplied text, plus a “contact HR when” section for exceptions, leave, accommodations, or unclear cases. Cite the relevant policy section after each answer. Do not add legal interpretations, promise eligibility, or resolve individual employee cases. List any important question the source text does not answer.
Survey analysis
Use this when you have anonymized engagement results and need priorities.
Analyze anonymized employee-survey data from [SURVEY DATA] alongside [PRIOR SURVEY DATA] and [BUSINESS CONTEXT]. Produce an executive summary, response-rate caveats, strongest and weakest themes, segment comparisons only where privacy thresholds are met, open-text themes with representative paraphrases, and three interventions with owners and success measures. Separate observed results from hypotheses. Do not infer protected characteristics, identify individuals, or treat small-group results as reliable. Flag where employee-relations review is needed before sharing findings.
Tips
- Use a structured scorecard before interviews begin; retrospective AI summaries cannot repair inconsistent questions or missing evidence.
- Remove names, health information, accommodation details, and other unnecessary employee identifiers before using any AI system.
- Keep policy prompts tied to the current approved text and route exceptions to HR rather than allowing a generated answer to become policy.
- For performance summaries, require examples, dates, and agreed expectations so the draft does not convert vague impressions into formal claims.
FAQ
Can AI rank candidates for us?
It should not make or automate employment decisions. It can organize job-related evidence for trained reviewers using a consistent scorecard.
Can we paste employee records into a public chatbot?
Usually no. Follow your security, privacy, retention, and vendor-approval rules, and minimize personal data in any approved system.
Can AI answer employee policy questions?
It can draft answers from approved policy text, but HR should handle exceptions, leave, accommodations, and jurisdiction-specific issues.